R use ddply or aggregate
I have a data frame with 3 columns: custId, saleDate, DelivDateTime.
> head(events22)
custId saleDate DelivDate
1 280356593 2012-11-14 14:04:59 11/14/12 17:29
2 280367076 2012-11-14 17:04:44 11/14/12 20:48
3 280380097 2012-11-14 17:38:34 11/14/12 20:45
4 280380095 2012-11-14 20:45:44 11/14/12 23:59
5 280380095 2012-11-14 20:31:39 11/14/12 23:49
6 280380095 2012-11-14 19:58:32 11/15/12 00:10
Here's the dput:
> dput(events22)
structure(list(custId = c(280356593L, 280367076L, 280380097L,
280380095L, 280380095L, 280380095L, 280364279L, 280364279L, 280398506L,
280336395L, 280364376L, 280368458L, 280368458L, 280368456L, 280368456L,
280364225L, 280391721L, 280353458L, 280387607L, 280387607L),
saleDate = structure(c(1352901899.215, 1352912684.484, 1352914714.971,
1352925944.429, 1352925099.247, 1352923112.636, 1352922476.55,
1352920666.968, 1352915226.534, 1352911135.077, 1352921349.592,
1352911494.975, 1352910529.86, 1352924755.295, 1352907511.476,
1352920108.577, 1352906160.883, 1352905925.134, 1352916810.309,
1352916025.673), class = c("POSIXct", "POSIXt"), tzone = "UTC"),
DelivDate = c("11/14/12 17:29", "11/14/12 20:48", "11/14/12 20:45",
"11/14/12 23:59", "11/14/12 23:49", "11/15/12 00:10", "11/14/12 23:35",
"11/14/12 22:59", "11/14/12 20:53", "11/14/12 19:52", "11/14/12 23:01",
"11/14/12 19:47", "11/14/12 19:42", "11/14/12 23:31", "11/14/12 23:33",
"11/14/12 22:45", "11/14/12 18:11", "11/14/12 18:12", "11/14/12 19:17",
"11/14/12 19:19")), .Names = c("custId", "saleDate", "DelivDate"
), row.names = c("1", "2", "3", "4", "5", "6", "7", "8", "9",
"10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20"
), class = "data.frame")
I'm trying to find the DelivDate for the most recent saleDate for each custId.
I can do that using plyr::ddply like this:
dd1 <-ddply(events22, .(custId),.inform = T, function(x){
x[x$saleDate == max(x$saleDate),"DelivDate"]
})
My question is whether there is a faster way to do this as the ddply method is a bit time consuming (the full data set is ~ 400k lines). I've looked at using aggregate() but don't know how to get a value other than the one I'm sorting by.
Any suggestions?
EDIT:
Here's the benchmark results for 10k lines @ 10 iterations:
test replications elapsed relative user.self
2 AGG2() 10 5.96 1.000 5.93
1 AGG1() 10 20.87 3.502 20.75
5 DATATABLE() 10 61.32 1 60.31
3 DDPLY() 10 80.04 13.430 79.63
4 DOCALL() 10 90.43 15.173 88.39
EDIT2 : While being quickest AGG2() doesn't give the correct answer.
> head(agg2)
custId saleDate DelivDate
1 280336395 2012-11-14 16:38:55 11/14/12 19:52
2 280353458 2012-11-14 15:12:05 11/14/12 18:12
3 280356593 2012-11-14 14:04:59 11/14/12 17:29
4 280364225 2012-11-14 19:08:28 11/14/12 22:45
5 280364279 2012-11-14 19:47:56 11/14/12 23:35
6 280364376 2012-11-14 19:29:09 11/14/12 23:01
> agg2 <- AGG2()
> head(agg2)
custId DelivDate
1 280336395 11/14/12 17:29
2 280353458 11/14/12 17:29
3 280356593 11/14/12 17:29
4 280364225 11/14/12 17:29
5 280364279 11/14/12 17:29
6 280364376 11/14/12 17:29
> agg2 <- DDPLY()
> head(agg2)
custId V1
1 280336395 11/14/12 19:52
2 280353458 11/14/12 18:12
3 280356593 11/14/12 17:29
4 280364225 11/14/12 22:45
5 280364279 11/14/12 23:35
6 280364376 11/14/12 23:01
I, too, would recommend data.table here, but since you asked for an aggregate solution, here is one which combines aggregate and merge to get all the columns:
merge(events22, aggregate(saleDate ~ custId, events22, max))
Or just aggregate if you only want the "custId" and "DelivDate" columns:
aggregate(list(DelivDate = events22$saleDate),
list(custId = events22$custId),
function(x) events22[["DelivDate"]][which.max(x)])
Finally, here's an option using sqldf:
library(sqldf)
sqldf("select custId, DelivDate, max(saleDate) `saleDate`
from events22 group by custId")
Benchmarks
I'm not a benchmarking or data.table expert, but it surprised me that data.table is not faster here. My suspicion is that the results would be quite different on a larger dataset, say for instance, your 400k lines one. Anyway, here's some benchmarking code modeled after @mnel's answer here so you can do some tests on your actual dataset for future reference.
library(rbenchmark)
First, set up your functions for what you want to benchmark.
DDPLY <- function() {
x <- ddply(events22, .(custId), .inform = T,
function(x) {
x[x$saleDate == max(x$saleDate),"DelivDate"]})
}
DATATABLE <- function() { x <- dt[, .SD[which.max(saleDate), ], by = custId] }
AGG1 <- function() {
x <- merge(events22, aggregate(saleDate ~ custId, events22, max)) }
AGG2 <- function() {
x <- aggregate(list(DelivDate = events22$saleDate),
list(custId = events22$custId),
function(x) events22[["DelivDate"]][which.max(x)]) }
SQLDF <- function() {
x <- sqldf("select custId, DelivDate, max(saleDate) `saleDate`
from events22 group by custId") }
DOCALL <- function() {
do.call(rbind,
lapply(split(events22, events22$custId), function(x){
x[which.max(x$saleDate), ]
})
)
}
Second, do the benchmarking.
benchmark(DDPLY(), DATATABLE(), AGG1(), AGG2(), SQLDF(), DOCALL(),
order = "elapsed")[1:5]
# test replications elapsed relative user.self
# 4 AGG2() 100 0.285 1.000 0.284
# 3 AGG1() 100 0.891 3.126 0.896
# 6 DOCALL() 100 1.202 4.218 1.204
# 2 DATATABLE() 100 1.251 4.389 1.248
# 1 DDPLY() 100 1.254 4.400 1.252
# 5 SQLDF() 100 2.109 7.400 2.108
The fastest between ddply and aggregate, I suppose would be aggregate, especially on huge data as you have. However, the fastest would be data.table.
require(data.table)
dt <- data.table(events22)
dt[, .SD[which.max(saleDate),], by=custId]
From ?data.table: .SD is a data.table containing the subset of x's
Data for each group, excluding the group column(s).
This should be pretty fast but data.table is likely faster:
do.call(rbind,
lapply(split(events22, events22$custId), function(x){
x[which.max(x$saleDate), ]
})
)
Here's a much faster data.table function:
DATATABLE <- function() {
dt <- data.table(events, key=c('custId', 'saleDate'))
dt[, maxrow := 1:.N==.N, by = custId]
return(dt[maxrow==TRUE, list(custId, DelivDate)])
}
Note that this function creates a data.table and sorts the data, which is a step you'd only need to perform once. If you remove this step (perhaps you have a multi-step data processing pipeline, and create the data.table once, as a first step), the function is more than twice as fast.
I also modified all the previous functions to return the result, for easier comparison:
DDPLY <- function() {
return(ddply(events, .(custId), .inform = T,
function(x) {
x[x$saleDate == max(x$saleDate),"DelivDate"]}))
}
AGG1 <- function() {
return(merge(events, aggregate(saleDate ~ custId, events, max)))}
SQLDF <- function() {
return(sqldf("select custId, DelivDate, max(saleDate) `saleDate`
from events group by custId"))}
DOCALL <- function() {
return(do.call(rbind,
lapply(split(events, events$custId), function(x){
x[which.max(x$saleDate), ]
})
))
}
Here's the results for 10k rows, repeated 10 times:
library(rbenchmark)
library(plyr)
library(data.table)
library(sqldf)
events <- do.call(rbind, lapply(1:500, function(x) events22))
events$custId <- sample(1:nrow(events), nrow(events))
benchmark(a <- DDPLY(), b <- DATATABLE(), c <- AGG1(), d <- SQLDF(),
e <- DOCALL(), order = "elapsed", replications=10)[1:5]
test replications elapsed relative user.self
2 b <- DATATABLE() 10 0.13 1.000 0.13
4 d <- SQLDF() 10 0.42 3.231 0.41
3 c <- AGG1() 10 12.11 93.154 12.03
1 a <- DDPLY() 10 32.17 247.462 32.01
5 e <- DOCALL() 10 56.05 431.154 55.85
Since all the functions return their results, we can verify they all return the same answer:
c <- c[order(c$custId),]
dim(a); dim(b); dim(c); dim(d); dim(e)
all(a$V1==b$DelivDate)
all(a$V1==c$DelivDate)
all(a$V1==d$DelivDate)
all(a$V1==e$DelivDate)
/Edit: On the smaller, 20 row dataset, data.table is still the fastest, but by a thinner margin:
test replications elapsed relative user.self
2 b <- DATATABLE() 100 0.22 1.000 0.22
3 c <- AGG1() 100 0.42 1.909 0.42
5 e <- DOCALL() 100 0.48 2.182 0.49
1 a <- DDPLY() 100 0.55 2.500 0.55
4 d <- SQLDF() 100 1.00 4.545 0.98
/Edit2: If we remove the data.table creation from the function we get the following results:
dt <- data.table(events, key=c('custId', 'saleDate'))
DATATABLE2 <- function() {
dt[, maxrow := 1:.N==.N, by = custId]
return(dt[maxrow==TRUE, list(custId, DelivDate)])
}
benchmark(a <- DDPLY(), b <- DATATABLE2(), c <- AGG1(), d <- SQLDF(),
e <- DOCALL(), order = "elapsed", replications=10)[1:5]
test replications elapsed relative user.self
2 b <- DATATABLE() 10 0.09 1.000 0.08
4 d <- SQLDF() 10 0.41 4.556 0.39
3 c <- AGG1() 10 11.73 130.333 11.67
1 a <- DDPLY() 10 31.59 351.000 31.50
5 e <- DOCALL() 10 55.05 611.667 54.91